docstr: Args:
      ap (TTS.tts.utils.AudioProcessor): audio processor object.
      meta_data (list): list of dataset instances.
      seq_len (int): voice segment length in seconds.
      verbose (bool): print diagnostic information.
docstr_tok: ["Args",":","ap","(","TTS.tts.utils.AudioProcessor",")",":","audio","processor","object",".","meta_data","(","list",")",":","list","of","dataset","instances",".","seq_len","(","int",")",":","voice","segment","length","in","seconds",".","verbose","(","bool",")",":","print","diagnostic","information","."]
code: def __init__(
      self,
      ap,
      meta_data,
      voice_len=1.6,
      num_speakers_in_batch=64,
      storage_size=1,
      sample_from_storage_p=0.5,
      additive_noise=0,
      num_utter_per_speaker=10,
      skip_speakers=False,
      verbose=False,
  ):
      """
      Args:
          ap (TTS.tts.utils.AudioProcessor): audio processor object.
          meta_data (list): list of dataset instances.
          seq_len (int): voice segment length in seconds.
          verbose (bool): print diagnostic information.
      """
      self.items = meta_data
      self.sample_rate = ap.sample_rate
      self.voice_len = voice_len
      self.seq_len = int(voice_len * self.sample_rate)
      self.num_speakers_in_batch = num_speakers_in_batch
      self.num_utter_per_speaker = num_utter_per_speaker
      self.skip_speakers = skip_speakers
      self.ap = ap
      self.verbose = verbose
      self.__parse_items()
      self.storage = queue.Queue(
          maxsize=storage_size * num_speakers_in_batch
      )
      self.sample_from_storage_p = float(sample_from_storage_p)
      self.additive_noise = float(additive_noise)
      if self.verbose:
          print("\n > DataLoader initialization")
          print(f" | > Speakers per Batch: {num_speakers_in_batch}")
          print(
              f" | > Storage Size: {self.storage.maxsize} speakers, each with {num_utter_per_speaker} utters"
          )
          print(
              f" | > Sample_from_storage_p : {self.sample_from_storage_p}"
          )
          print(f" | > Noise added : {self.additive_noise}")
          print(f" | > Number of instances : {len(self.items)}")
          print(f" | > Sequence length: {self.seq_len}")
          print(f" | > Num speakers: {len(self.speakers)}")
  
file_path: TTS/TTS/speaker_encoder/dataset.py
imports: import numpy
  import numpy
  import queue
  import torch
  import random
  from torch.utils.data import Dataset
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new MyDataset object from inherited base classes: Dataset with example: obj = MyDataset(ap, meta_data, voice_len, num_speakers_in_batch, storage_size, sample_from_storage_p, additive_noise, num_utter_per_speaker, skip_speakers, verbose)